The Transition of Military Veterans from Active Service to Civilian Life
Abstract: In every nation within NATO, service members at some point leave the military. The military-to-civilian transition is the term used to refer to the process by which service members and/or their families rejoin their civilian community. Transition out of the military includes a series of adjustments. These include changes in location, career, relationships, family roles, support systems, social networks, community and culture. This transition has implications for post-service well-being and functioning. Despite this little has been done to conceptualize how transition occurs, identify factors that promote or impede transition, or operationalize outcomes associated with transition success. Many veterans transitioning from the military to the civilian life encounter unexpected challenges such as finding meaningful employment, adjusting to "civilian" culture or dealing with unresolved mental and physical health issues. In this report, we present the current practices and policies of military-to-civilian for those nations who participated in this RTG. In addition, we also present the findings of a survey of NATO nations conducted focusing on practices and policies of NATO and non-NATO nations. Nine key principles were identified that very nation, both NATO and non-NATO nations, should consider in supporting service members and families in re-joining their civilian community.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.